Collaborative Optimization Model for Green Power Trading Considering Environmental Value in Multiaggregator Systems

To address the issue of coordinated optimization of environmental and economic benefits in the green power market, this paper proposes an aggregator collaborative optimization model that incorporates environmental value in game-theoretic trading. By integrating environmental value attributes, the model quantifies the substitution effect of renewable energy on coalfired power generation and constructs a green electricity trading framework that includes both positive and negative environmental values, forming a cooperative alliance with complementary supply and demand among multiple aggregators. Based on asymmetric Nash bargaining theory, a fair benefit distribution mechanism is designed, and distributed collaborative optimization is achieved using the Alternating Direction Method of Multipliers (ADMM). The model introduces a carbon emission correction factor to dynamically assess the environmental benefits of aggregators under different supply and demand conditions, optimizing the trading process through power balance, energy storage constraints, and inter-regional complementary dispatching. Through case verification, the proposed model can effectively improve the green power absorption efficiency and system benefits, providing theoretical and methodological support for the efficient operation of the green power market.